ABSTRACT Breast cancer rates among African-American (AA) women continue to rise and may further widen breast cancer disparities experienced by AA women, who are more likely to develop aggressive tumor types with a worse prognosis. The biological reasons for these differences remain largely unknown. Recent genome-wide, high-throughput studies highlight an emerging role of long noncoding RNAs (lncRNAs) as a novel class of regulatory molecules in cancer. LncRNAs form an important regulatory layer in global gene expression, and increasing evidence indicates that abnormal expression of specific lncRNAs can contribute to breast cancer carcinogenesis and progression. Studies to date, however, are focused exclusively on EA women, have not commonly used high-throughput next generation sequencing (NGS) to provide unbiased comprehensive profiling, and mostly do not incorporate rigorous normal tissue controls. Motivated by these research gaps and limitations, we recently completed a pilot study of genome-wide lncRNA expression profiling in normal and tumor breast tissues from AA and EA women. LncRNA expression data showed clear tissue- and subtype- specific expression patterns. Importantly, we noted a number of differentially abundant lncRNAs between AA and EA women by estrogen receptor (ER) status. These results indicate that there are unique lncRNA expression patterns in AA tumors, which we hypothesize contributes to aggressive tumor biology and high breast cancer-related mortality. We propose a cost-effective study in a well-characterized cohort of AA breast cancer patients in the Women?s Circle of Health Study (WCHS), which has available tumor tissue blocks, and extensive data on tumor characteristics, clinical outcomes, treatments received, lifestyle factors, and genome- wide DNA methylation. As such, our Specific Aims are: 1) Perform tissue lncRNA expression profiling using total RNA sequencing (1181 AA cases from WCHS and 100 AA controls from Komen Tissue Bank) to determine lncRNAs that are breast cancer- and ER subtype- specific (tumor, ER+, ER- vs. normal) and those associated with clinico-pathological factors (e.g., grade); 2) Examine associations of lncRNA expression levels with breast cancer survival, and use a machine learning approach to identify a combined panel of lncRNAs associated with breast cancer survival; and further perform computational prediction and in vitro functional assays to determine their biological relevance; and 3) Integrate paired data on lncRNA expression and DNA methylation to determine which of these cancer- and prognosis-relevant lncRNAs are regulated by DNA methylation, and explore whether diet, obesity and other lifestyle-related factors are associated with aberrant DNA methylation. This work is novel and findings are anticipated to advance our understanding of molecular mechanisms contributing to aggressive tumor biology and poor cancer prognosis observed in AA women that can be translated into the development of targeted strategies for prevention and therapeutics.